This thesis explores the economic feasibility of deploying generative AI chatbots at Guaranty Trust Bank (GTBank) in Nigeria. Using a five-year cost-benefit analysis, it assesses financial returns, operational efficiency, and customer service improvements. The study identifies key cost categories—software, infrastructure, training, and maintenance—against measurable benefits like reduced labor costs and enhanced digital engagement. Results show a positive net present value and a strong benefit-cost ratio, with break-even projected within three to four years. The research concludes that AI chatbot deployment offers not only economic value but also strategic advantages in Nigeria’s increasingly competitive digital banking landscape.
Table of Contents
Executive Summary
Introduction & Problem Statement
Literature Review & Theoretical Framework
Methodology or Analytical Approach
Economic Analysis (including demand/supply, cost, and market structure components)
Managerial Implications and Strategic Recommendations
Conclusion & Reflections…
Appendices
References
Glossary
Executive Summary
This thesis evaluates the economic viability of deploying generative AI chatbots at Guaranty Trust Bank (GTBank) within Nigeria’s evolving digital banking landscape. In response to increasing operational pressures, including high personnel costs and the demand for 24/7 customer service, this study applies a quantitative cost-benefit analysis (CBA) framework over five years (2025–2029). By integrating secondary data from GTBank’s recent investor materials with industry benchmarks, the analysis estimates both the upfront capital and ongoing operational costs, as well as the anticipated benefits in operational efficiency, labor cost savings, and customer engagement improvements.
The cost structure is detailed in five categories: software licensing, infrastructure, system integration, training and change management, and maintenance and support. Initial investments are front-loaded, with significant costs incurred in Year 1 for licensing and integration, followed by a stabilization of expenses in subsequent years. Conversely, the benefits are modeled to ramp up progressively, reflecting improvements in digital engagement, reduced staffing requirements, and increased revenue from enhanced digital channels. Preliminary calculations indicate a positive net present value (NPV) and a benefit-cost ratio (BCR) well above one, with the investment reaching break-even point between Years 3 and 4.
Beyond the financial metrics, the study also examines the strategic implications of generative AI adoption. Deploying AI chatbots is positioned not only as a cost-saving measure but also as a strategic tool enabling GTBank to secure a competitive advantage in Nigeria’s oligopolistic banking market. The research concludes by outlining key managerial implications, including the need for robust change management, proactive risk mitigation strategies for potential technical failures and customer backlash, and complementary investments in training and AI governance. The findings suggest that a strategic investment in generative AI chatbots can deliver significant economic and competitive benefits for GTBank.
Chapter 1 Introduction
1.1 Background to Study
The global banking industry has undergone considerable transformation in the last two decades, particularly in response to financial crises, tightening regulatory frameworks, and the rise of digital technologies (IBM IBV 2025). Technological innovation has become central to strategic planning and operations as banks strive to remain competitive and responsive to customer expectations. Artificial intelligence (AI), including machine learning and generative models, is now a major driver of innovation in areas such as fraud detection, credit scoring, customer service, and operational efficiency (PwC 2024; KPMG 2024).
In Nigeria, digital banking adoption has grown rapidly due to mobile phone penetration, fintech disruption, and cashless policies by the Central Bank of Nigeria (CBN) (TechCabal Insights, 2024; Statista 2024). Traditional banks have responded by launching mobile banking applications, USSD platforms, and online services. However, beyond basic payments and transfers, there remains a gap in the adoption of generative AI technologies, particularly for customer engagement and service automation. While a few institutions like Zenith Bank (ZiVA) and UBA (Leo) have launched chatbot services, many, including GTBank, have yet to implement customer-facing generative AI systems despite leading market capitalization and digital infrastructure (Zenith Bank media 2021; TechCabal Insights 2024).
1.2 Problem Statement
The current surge in digital transaction volumes in Nigeria, reflected in a record-breaking ₦1.07 quadrillion in electronic transactions via NIBSS in 2024 (Nairametrics, 2025), shows how far digital banking has come. Yet, most traditional banks still rely on human-centered customer service operations, which are expensive, time-bound, and prone to inconsistency. Generative AI chatbots, powered by large language models, present a cost-efficient and scalable alternative that can revolutionize 24/7 customer service (PwC 2024). The problem is whether the economic returns from such an investment justify the upfront and ongoing costs, particularly in a high-risk, regulation-sensitive sector like banking.
1.3 Research Question
Is the deployment and scaling of generative AI chatbots across GTBank’s customer service channels economically justifiable based on measurable financial and strategic returns over a five-year horizon?
1.4 Research Objectives
1. To evaluate the projected costs and benefits of implementing generative AI-powered chatbots in GTBank
2. To calculate financial metrics such as Net Present Value (NPV), Internal Rate of Return (IRR), and Benefit-Cost Ratio (BCR)
3. To assess how AI-driven automation influences customer engagement, operational efficiency, and market competitiveness
4. To examine strategic risks and implementation challenges associated with generative AI in the Nigerian banking context
1.5 Scope of the Study
This study focuses on GTBank as a case subject due to its leading position in the Nigerian banking sector and its strong digital infrastructure. The cost-benefit analysis is framed over a five-year investment horizon (2025–2029) and focuses specifically on the deployment of generative AI chatbots for customer service automation.
1.6 Justification of the Study
For Nigerian banks, which are navigating increasing competition from Neobanks and fintechs, this research offers practical insights into whether investing in generative AI is worth the risk. The findings will help inform technology investment strategies, regulatory planning, and customer experience innovation in emerging markets.
Chapter 2 Theoretical Framework and Literature Review
This chapter presents a comprehensive review of existing literature and theoretical perspectives relevant to the deployment of generative artificial intelligence (AI) chatbots in the Nigerian banking industry.
2.1 Global and Regional Trends in Digital Transformation within the Banking Industry
Globally, the banking industry has experienced significant transformation over the past two decades, driven by a confluence of technological innovation, evolving consumer expectations, and regulatory evolution. Since the 2008 Global Financial Crisis, banks around the world have had to adapt through aggressive restructuring, compliance overhauls, interest rate adjustments, and accelerated innovation to maintain competitiveness and ensure operational resilience (IBM, 2025). According to McKinsey, and the Statista Banking Worldwide Outlook (2024), the global banking industry remains one of the most profitable sectors, with Net Interest Income (NII) projected to reach US$8.94 trillion by 2025 and maintain a compounded annual growth rate (CAGR) of 4.91% between 2025 and 2029. Despite this profitability, the sector faces growing uncertainty amid geopolitical tensions and rapid technological evolution (McKinsey, 2025). Consequently, banks are under pressure to adopt new technologies that increase efficiency, reduce cost, and meet rising consumer demands for speed, flexibility, and personalization.
A notable shift has been the growth of digital banking, spurred further by the COVID-19 pandemic. Global lockdowns and mobility restrictions accelerated the adoption of mobile applications, contactless transactions, and remote banking services. This transformation gave rise to Neobanks and fintech disruptors, which now challenge traditional banking models by offering fully digital, user-centric experiences with low overhead costs (McKinsey, 2023). Within the global banking landscape, the market can be divided based on services offered, such as retail and commercial banking, and the channels through which services are delivered. Traditional banks typically offer their services through physical branches and legacy systems, while digital-only banks operate primarily through mobile apps and online portals (Statista, 2024).
In Nigeria, the digital transformation of banking has accelerated sharply since 2020. While traditional banks still dominate the market, their expansion into digital banking channels has driven significant market growth. Statista (2024) projects that Nigeria’s traditional banking market will reach a volume of US$24.94 billion by 2025, with a steady CAGR of 3.29% in net interest income between 2025 and 2029. Moreover, TechCabal Insights (2025) reports that the total banking sector volume in Nigeria surged from ₦3.2 trillion in 2020 to a projected ₦10.5 trillion in 2025–an increase of over 224%. This growth is largely attributed to increased digital adoption among traditional banks, the emergence of fintech and Neobank providers (e.g., Opay, PalmPay, Kuda), and cashless policy reforms from the Central Bank of Nigeria.
Digital transformation in Nigeria has extended beyond payments and transfers. Technologies like predictive analytics, artificial intelligence (AI), and mobile money platforms are redefining customer experiences and enabling personalized financial services. As digital banking adoption grows, traditional banks are increasingly under pressure to innovate further, not only to retain their customer base but also to remain competitive amid rising fintech disruption.
2.2 Overview of Guaranty Trust Bank (GTBank)
Guaranty Trust Bank (GTBank) is a prominent Nigerian multinational financial institution providing commercial banking services to individuals, small businesses, corporations, and public institutions across Africa and the United Kingdom. Established in 1990, GTBank has evolved into a key player in the African financial sector through its emphasis on innovation, operational efficiency, and customer-focused services (GTBank, 2024). The bank operates in Nigeria and maintains subsidiaries in Ghana, Sierra Leone, The Gambia, Liberia, Kenya, Rwanda, Tanzania, Uganda, and Côte d'Ivoire, with an international presence in the United Kingdom. It offers a broad range of services including retail, commercial, corporate, and investment banking (GTBank, 2024a). GTBank is distinguished by its customer-centric model, which emphasizes seamless service delivery, minimal bureaucracy, and user-friendly digital banking experiences. This strategy is driven by the institution’s core operational principles known as “The Orange Rules,” which emphasize Simplicity, Professionalism, Service, Friendliness, Excellence, Trustworthiness, Social Responsibility, and Innovation (GTBank, 2023). In alignment with these values, GTBank has made significant investments in digital technology, with platforms such as GTPay and GTWorld playing central roles in expanding customer access and driving e-channel revenue growth. These innovations reflect the bank’s ongoing commitment to digital transformation in financial services (GTBank, 2024).
According to the bank’s 2024 half-year audited results, Profit Before Tax reached ₦1.004 trillion, a 206.6% increase from ₦327.4 billion in the same period of 2023; Net Loan Book rose by 25.5% from ₦2.48 trillion to ₦3.11 trillion; Deposit Liabilities increased by 39.8% from ₦7.55 trillion to ₦10.55 trillion; Total Assets stood at ₦14.5 trillion; Shareholders’ Funds totaled ₦2.4 trillion; The bank maintained a Capital Adequacy Ratio (CAR) of 21.0%, an IFRS 9 Stage 3 Loan ratio of 4.3%, and a Cost of Risk (COR) of 1.6% (GTBank, 2024a). GTBank’s consistent performance and recognized leadership in digital banking and CSR have earned it accolades such as Nigeria’s leading bank brand in 2023 (Statista 2024). The bank’s strong financial foundation, combined with its digital infrastructure, positions it as an ideal choice for evaluating the cost-benefit implications of deploying generative AI chatbots in a Nigerian banking context.
2.3 Cost-Benefit Analysis (CBA)
Cost Benefit Analysis (CBA), also known as Benefit-Cost Analysis (BCA) dates back to its introduction by Abbé de Saint-Pierre and popularized by French Engineer, Jules Dupuit, via his pioneering work, which assessed the relative cost and benefit of building roads and bridges (Jiang & Marggraf, 2021). According to Boardman et al, 2011, Cost Benefit Analysis is an approach to measuring the relative viability of a proposal by assessing its relative benefits and costs in weighted terms (dollars). In other words, CBA answers the question: Is this project a good expenditure on our available resources? In this study, it is applied within the context of strategic IT investment. There are various approaches to interpreting a cost-benefit analysis depending on the entity's goal. They include present values of costs and benefits; net benefits, derived by subtracting the present value costs from the present value benefits; and a benefit-cost ratio, which involves dividing the present value benefits by the present value costs. A positive net benefit and a benefit-cost ratio of 1.0 or higher indicate that the benefits of the investment are equal to or greater than the costs. (Sandal National Laboratories, 2021). Traditional banks like Guaranty Trust Bank are faced with increasing pressure to remain resilient, adaptable, profitable, and client-centric in this technologically advanced age fraught with uncertainty and risk. Cost-Benefit Analysis can be a strategic investment appraisal technique to assess the relative benefits of exploring some of the technological trends springing up daily.
2.4 Production Theory
Another theoretical framework that is central to our analysis is Production Theory. This theory discusses how firms transform inputs into outputs to maximize profit. The main goal of firms is to maximize profit. The traditional production function is expressed functionally as Y = f(K, L). Where Y = output; K = capital, L = Labor. Output is a function of capital and labor. However, since the advent of technological advancements, the production function has been influenced by technological inputs. This is relevant to our analysis as generative AI chatbots can be seen as technological inputs that enable efficiency. Banks can serve more customers better with the same or fewer resources. The production function now takes the Cobb-Douglas form (Mandal and Taku 2025; Al-Sawaie et al 2025): Y = A . f(K, L) or, more specifically, Y = A . Kα. Lβ Where: A = Total Factor Productivity (representing technological advancement), and , and = Output elasticities of capital and labor, respectively
2.5 Cost Theory
Cost theory explores the relationship between fixed and variable cost in a production process. The cost function is derived from the production function. It explores the relationship between total cost of production and input involved in the production process. This can be functionally expressed as: C = f(Q, T, pf, K). Where Q = output level; T = level of technology; pf = prices of factors, and k is fixed cost. Another way to rewrite this, which is highly relevant to our analysis, is that total cost (C) is influenced by the levels of fixed and variable costs. Thus, C = FC + TVC. Where FC = fixed cost; TVC = Total variable cost. The fixed costs involved in deploying gen AI chatbot in banking include development and deployment cost, integration costs, design and user interface costs, hosting and infrastructure costs. Variable costs for deploying a Gen AI chatbot in banking can include ongoing maintenance and support, as well as the costs associated with training and retraining the AI model with new data. Additionally, expenses related to cloud infrastructure, API usage, and data storage can fluctuate based on usage and complexity (KPMG 2025).
Additionally, with AI chatbots banks can convert certain variable customer service costs into fixed costs, reducing marginal cost per interaction over time. With AI chatbot automation in banking, marginal costs can be significantly decreased as the chatbot handles more and more customer interactions. Last, average costs may initially increase due to the initial development and implementation expenses, but eventually decline as the chatbot's efficiency and effectiveness improve. Additionally, economies of scale with generative AI (GenAI) chatbots in banking can lead to significant cost savings, potentially reducing customer service costs by 30-40% and potentially saving banks billions globally. (Marous 2024; Adeyeri 2025; Fox 2025)
2.6 Market Structure and Competition
The Nigerian banking industry exhibits characteristics of an oligopolistic market, dominated by a few key players with differentiated services (Bello & Isola 2014). This means that a small number of large banks control a significant portion of the market, influencing pricing, product offerings, and overall competition. While smaller banks exist, the top few dominate the industry (Okelue et al, 2012). Acronymized FUGAZ, the five traditional banks in Nigeria with the largest market capitalization crossing N1 trillion, alongside a thriving customer base, include: F = FirstBank of Nigeria; U = United Bank for Africa; G = Guaranty Trust Bank; Z = Zenith Bank. Generative AI models can help these banks identify possible risk areas and preserve profitability by analyzing historical data patterns, market trends, and simulating different economic scenarios.
2.7 Decision-Making Under Uncertainty
Risk and uncertainty are central to managerial decisions, especially with emerging technologies. Investment in AI carries adoption risks, regulatory uncertainty, and potential job displacement backlash. In economics, decision-making under uncertainty refers to the process of choosing among options where the consequences of those choices are not fully known. Banks face various risks when adopting new technologies, including cybersecurity breaches, data privacy violations, operational disruptions, and the potential for algorithmic bias.
2.8 Opportunity Cost
Money available today can be invested, generating returns and growing overtime. This growth potential is the opportunity cost of holding onto money instead of investing it. Opportunity cost represents the value of the next best alternative foregone when a decision is made. For GTBank, investing in AI chatbots must be justified not just by absolute returns, but by comparison to potential gains from other digital projects. Alternatives to deploying AI chatbots include using platforms that offer pre-built chatbot features, integrating live chat with AI assistance, or leveraging other forms of AI-powered customer service like help desks and shared inboxes. (lsm 2025)
2.9 Discounting
Discounting adjusts future costs and benefits their present values, enabling apples-to-apples comparison over time. (Investopedia 2024). A discount rate is the opportunity cost of capital and the risk of the investment. It significantly impacts Net Present Value (NPV) calculations. Higher discount rates lower the present value of future cash flows, resulting in a lower NPV and making the investment less attractive. Km
2.10 Externalities
Externalities are costs or benefits from economic activity that affect third parties and are not reflected in market prices (Econlib). While AI chatbots may displace some jobs, they also have the potential to expand banking access and reduce service disparities. The spillovers from GenAI chatbot in traditional banking can be both positive and negative. Positively, chatbots can enhance customer support, improve efficiency, and personalize banking experiences. They can also boost financial literacy by making complex concepts easier to understand. Negatively, there are concerns about data privacy and security, the potential for biased or misleading information, and the risk of chatbot misuse. (Wu 2024; Alargasamy & Mehrolia 2023).
Chapter 3 Methodology or Analytical Approach
This thesis adopts a quantitative cost-benefit analysis (CBA) framework to evaluate the economic viability of deploying generative AI-powered chatbots at Guaranty Trust Bank (GTBank). The methodology combines secondary data and reasonable assumptions to estimate both the expected costs and projected benefits over a defined investment period.
3.1 Research Design
The analysis is structured as a deterministic financial model using standard economic evaluation tools: Net Present Value (NPV) to measure long-term value creation; Benefit-Cost Ratio (BCR) to assess investment efficiency; Break-even Analysis to determine the timeframe required to recover initial costs The model is designed for a five-year time horizon (2025–2029) and all costs and revenues are presented in the U.S. Dollars, using an assumed average exchange rate of ₦1,700 to $1 USD based on current macroeconomic trends. A discount rate of 10% is applied to account for inflation, risk, and Nigeria’s average weighted average cost of capital (WACC).
3.2 Data Sources
This study relies on secondary data from industry reports (GTCO, McKinsey, PwC, Deloitte), published reports, and case studies on chatbot adoption in financial services. Estimated assumptions are derived from market benchmarks, fintech pricing disclosures (e.g., OpenAI API, Azure, AWS pricing), and operational cost estimates within the Nigerian banking context. Where hard data is unavailable, conservative projections were made to avoid overestimating benefits or underestimating costs.
3.3 Cost Components
The categories of investment modeled include Software Licensing, Infrastructure, System Integration, Training & Change Management, and Maintenance.
3.4 Benefit Components
Estimated financial benefits fall into five main categories: Labor Cost Savings, Error Reduction & Compliance Gains, Faster Response Times, Customer Retention & Loyalty, and Revenue Growth
3.5 Key Assumptions and Scenarios
Key assumptions include: All costs and benefits accrue annually and consistently over the project duration; There is a 6-month ramp-up phase before full benefit realization begins; Chatbot deployment does not eliminate jobs, but allows for role reallocation and cost reductions; and No major regulatory or operational disruption occurs during the analysis window
Chapter 4 Economic Analysis (including demand/supply, cost, and market structure components)
This chapter assesses the economic viability of deploying generative AI chatbots at Guaranty Trust Bank (GTBank) nationwide over five years (2025–2029) using cost-benefit analysis (CBA). The analysis combines primary assumptions and secondary data, including benchmark figures and strategic insights drawn from GTCO’s Holdco Abridged March 2025 Report and the December 2024 Full-Year Investor Presentation to forecast both the costs and benefits of the project.
4.1 Cost Structure Analysis
Implementing a generative AI chatbot solution involves several cost components that can be broadly classified as fixed, variable, or semi-variable. Table 4.1 below details five primary cost areas and indicates how each is expected to behave over the five-year forecast.
4.1.1 Cost Categories:
- Software Licensing (Fixed- mostly): This cost includes annual access fees for large language model (LLM) APIs (e.g., Azure OpenAI or OpenAI Enterprise), along with customization and fine-tuning expenses. Enterprise licensing contracts are generally negotiated on a fixed annual fee basis to provide budgeting certainty. For example, GTBank, if it were to adopt an AI solution, would likely secure a multi-year fixed contract; even if usage-based pricing options exist, large-scale deployment favors fixed-fee agreements. The licensing cost is highest in Year 1 (due to initial procurement and setup (Ada.cx 2025) at approximately USD 250,000, then stabilizes at around USD 100,000 per year.
- Infrastructure (Variable): These are costs associated with cloud computing resources, including storage, compute time, and network usage, that support real-time chatbot interactions. Cloud infrastructure follows a “pay-as-you-go” model, meaning the expense scales with usage. As GTBank’s digital channels become more efficient or renegotiations occur with providers (e.g., AWS or Azure), these costs may moderate (AWS, 2023; Azure, 2024). Infrastructure costs start at roughly USD 120,000 in Year 1, then decrease or remain stable at about USD 60,000 per year as efficiency gains are realized.
- System Integration (Fixed – One-time): The cost required to integrate the new AI solution into GTBank’s existing systems (CRM, core banking, and support platforms). Integration is an upfront investment that does not vary with system usage once completed (McKinsey AI Bank of the Future Report 2021). We assume a one-time cost of USD 100,000 incurred in Year 1.
- Training and Change Management (Fixed – Short-term): This includes expenses for initial training of frontline staff, IT teams, and developers, as well as overall change management communications. Training costs are incurred as fixed investments during the rollout phase. Although retraining may occur intermittently, these expenses are largely fixed during the initial adoption period (AlphaBold 2025). Costs were estimated at USD 80,000 in Year 1, then declining to USD 40,000, USD 20,000, and finally USD 10,000 in subsequent years as staff become proficient.
- Maintenance and Support (Semi-variable): They feature ongoing expenses including vendor support, regular updates, cybersecurity measures, and periodic retraining of the AI system. Maintenance involves a fixed base service fee combined with variable elements that increase as service complexity grows (AlphaBold 2025). Starting at USD 50,000 in Year 1, increasing gradually to USD 60,000, USD 70,000, USD 80,000, and USD 90,000 over Years 1–5.
Table 4.1: Projected Annual Costs for AI Chatbot Deployment at GTBank (USD)
Illustrations are not included in the reading sample
This cost structure is informed by industry benchmarks and the digital transformation data presented in the investor materials (GTCO, 2025). The front-loaded cost in Year 1 reflects the high initial outlay for licensing, integration, and training, which then transitions to a lower, maintenance-focused expenditure in subsequent years.
4.2 Benefit Estimation
The benefits of deploying generative AI chatbots are driven mainly by improved operational efficiency and enhanced customer experience. Based on GTBank’s digital transformation metrics reported in the December 2024 Investor Presentation, this section projects the following benefit components over five years:
- Labor Cost Savings: A utomating routine customer inquiries reduces the need for call-center staff and improves service efficiency. We assume a reduction in personnel costs by a specific percentage (e.g., 15-20%) for processes that are fully automated. Industry studies from Deloitte (2023) suggest labor cost reductions ranging from 10–20% in similar deployments.
- Error Reduction & Compliance: Streamlined responses minimize human error, reducing costs from compliance fines and error-induced losses. Leverage internal performance reports and industry metrics (PwC, 2022) to quantify expected savings.
- Enhanced Customer Retention and Revenue Growth: Improved service availability (24/7 support) boosts customer retention, which in turn increases cross-selling and upselling opportunities. Revenue impacts are modeled based on a conservative increase in digital channel engagement rates, using growth figures reported in GTCO’s investor presentation.
- Operational Efficiency Gains: Faster query resolution leads to reduced waiting times and improved digital transaction processing, driving revenue indirectly. Using published benchmarks on chatbot efficiency gains, we assign a percentage improvement in operational metrics.
Table 4.2: Estimated Annual Benefits from AI Chatbot Deployment
Illustrations are not included in the reading sample
These projections align with industry trends showing that, when effectively implemented, AI solutions can dramatically improve service efficiency and customer engagement, thus creating substantial operational savings and revenue enhancements (PwC, 2022; McKinsey, 2024).
4.3 Financial Metrics Analysis
Table 4.3: Summarized CBA Table
Illustrations are not included in the reading sample
4.3.1 Net Present Value & Benefit-Cost Ratio
Illustrations are not included in the reading sample
Net Present Value (NPV) = −409,090.91 + 33,057.85 + 150,260.06 + 170,753.46 + 180,069.99 = $125,050.45
Illustrations are not included in the reading sample
Discounted Totals:
- Discounted Benefits (sum of present value of benefits): 150,000 × 0.9091 + 300,000 × 0.8264 + 450,000 × 0.7513 + 500,000 × 0.6830 + 550,000 × 0.6209 = 1,004,484.26
- Discounted Costs (sum of present value of costs): 600,000 × 0.9091 + 260,000 × 0.8264 + 250,000 × 0.7513 + 250,000 × 0.6830 + 260,000 × 0.6209 = 879,433.81
Illustrations are not included in the reading sample
4.3.1 Break-Even Analysis
The break-even analysis will determine the point (in years) when cumulative benefits equal or exceed cumulative costs. This analysis is important for understanding the timeframe in which the AI investment begins to generate a net positive financial impact. From the summarized CBA table, the breakeven point occurs in Year 4.
4.4 Demand and Supply Implications
Deploying AI chatbots influences the supply and demand of banking services in several ways. On the supply side, these tools expand service capacity by handling high volumes of customer interactions around the clock, without needing to scale human staff, effectively shifting the supply curve outward. On the demand side, better service availability and responsiveness attract more users, particularly younger, digitally inclined customers. As chatbot reliability improves and wait times shrink, satisfaction rises, encouraging greater use of the bank’s digital platforms
4.5 Market Structure and Strategic Positioning
GTBank operates within Nigeria’s oligopolistic banking sector, where a few large institutions dominate the market. Staying competitive depends heavily on innovation. Banks that embrace advanced technologies like generative AI can gain a clear first-mover advantage, improving efficiency, reducing costs, and delivering better customer experiences. While Zenith Bank has already launched its ZiVA chatbot, GTBank’s slower entry into space presents a unique chance to catch up strategically. GTBank can increase its market share and sharpen its digital edge by learning from early adopters and launching a refined solution. Meanwhile, evolving regulatory pressures and economic challenges such as inflation and exchange rate volatility are pushing banks to streamline operations. Deploying AI offers GTBank a practical and forward-looking way to respond to these pressures while reinforcing its position in a highly competitive market.
Chapter 5 Managerial Implications and Strategic Recommendations
This chapter outlines critical implications for leadership and proposes clear recommendations to ensure successful implementation.
5.1 ROI Timeline and Performance Milestones
Based on the cost-benefit analysis, the investment in generative AI chatbots is projected to yield net positive returns by Years 3 to 4. In practice, GTBank should set concrete milestones to monitor progress. Year 1 should feature c omplete system integration, initiate pilot testing, and conduct comprehensive staff training; by Year 2 aim to achieve measurable improvements in operational efficiency and reduced labor costs, laying the groundwork for broader implementation; by Year 3, reach break-even on cumulative investments while observing increased customer engagement and revenue growth; and finally from Year 4 onward c onsolidate digital gains, optimize operational processes, and realize robust NPV improvements, thereby sustaining competitive advantage.
5.2 Risk Mitigation Strategies
Transitioning to AI-driven customer service comes with risks, particularly around technical reliability and customer acceptance. System failures or integration issues can disrupt service, so it’s essential to work with trusted vendors, conduct thorough pilot testing, and build in system redundancies. On the customer side, a poorly performing chatbot may lead to frustration or backlash. To ease this transition, banks can start with simple tasks, ensure human support is available when needed, and use ongoing customer feedback to improve the AI’s performance over time.
5.3 Change Management and Workforce Adaptation
Effective AI adoption depends not just on technology, but on how well people within the organization adapt. For GTBank, this means clearly communicating the goals and benefits of AI across all levels, investing in continuous employee training, and building a culture that embraces innovation. Employees should feel empowered to contribute ideas, while leadership must actively champion the transition, reassuring teams about their roles and future within a digitally evolving bank.
5.4 Complementary Investments and Strategic Enhancements
To fully realize the benefits of AI chatbot deployment, GTBank should complement the technology with strategic investments. First, integrating chatbots across mobile and web platforms will ensure a smooth, consistent user experience. Second, a strong AI governance framework is essential to oversee performance, manage risk, and maintain compliance with ethical and regulatory standards. Third, embedding advanced analytics will enable real-time tracking of customer interactions and operational impact, supporting continuous improvement. Finally, exploring adjacent technologies, such as voice-enabled bots and AI-driven fraud detection, can enhance security, deepen customer trust, and strengthen the bank’s long-term digital strategy.
5.5 Strategic Recommendations
Based on the findings of this study, GTBank stands at a pivotal point where deploying generative AI chatbots can deliver significant strategic value, if implemented with care. A gradual rollout that starts with basic customer queries will allow for smoother integration while managing disruption. Human support should remain in place to supervise the transition and handle complex needs. Success will depend on tracking key metrics such as customer satisfaction, engagement, and ROI through a centralized dashboard. Sustained funding for innovation, training, and system upgrades is essential to keep the platform effective and competitive. Just as crucial is ongoing dialogue with regulators to ensure compliance and leadership in responsible AI use. While the path demands upfront investment and prudent risk management, the long-term benefits make it a smart and timely move for GTBank.
Chapter 6 Conclusion & Reflection
This thesis assessed the economic and strategic case for deploying generative AI chatbots at GTBank through a cost-benefit analysis backed by industry insights. Despite notable upfront costs, the projected labor savings, improved efficiency, and stronger customer engagement point to a positive net present value and benefit-cost ratio, with breakeven expected within 3 to 4 years. As digital transformation accelerates, AI chatbots offer GTBank a competitive edge, streamlining operations, enhancing 24/7 service, and positioning the bank to better compete with fintechs and adapt to regulatory and economic pressures. The findings also highlight broader opportunities for Nigerian banks to use AI in strengthening customer service and operational resilience. Future steps could include adding voice bots, AI-driven fraud detection, and personalized financial tools. Ultimately, when backed by strong change management, investments in generative AI stand to deliver lasting financial and strategic value.
Appendices
References
Ada. (2024). Cost reduction in banking through AI: How automation is redefining efficiency. https://www.ada.cx/blog/cost-reduction-in-banking-through-ai-how-automation-is-redefining-efficiency/
AlphaBOLD. (2024). Cost analysis: Implementing generative AI in your organization. https://www.alphabold.com/cost-analysis-implementing-generative-ai-in-your-organization/
Amazon Web Services. (2023). Understanding AWS pricing. https://aws.amazon.com/pricing/
Asana. (2025). Cost-benefit analysis: 5 steps to make better choices. https://asana.com/resources/cost-benefit-analysisAsana
Boardman, A., Greenberg, D., Vining, A., & Weimer, D. (2011). Cost Benefit Analysis: Concepts and Practice (5th ed.).
Brand Colossus. (2024). Nigeria’s fintech frontier: Chatbots revolutionizing digital banking in Nigeria. https://brandcolossus.com.ng/nigerias-fintech-frontier-chatbots-revolutionizing-digital-banking-in-nigeria/
Econlib. (2024). Market failures, public goods, and externalities. https://www.econlib.org/library/Topics/College/marketfailures.htmlEconlib
Everest Group. (2023). Navigating the landscape: The cost and benefits of generative AI implementation. https://www.everestgrp.com/financial-services-industry/navigating-the-landscape-the-cost-and-benefits-of-generative-ai-implementation-blog.html
Glazier, S. (2024). The opportunity cost of allocating capital to AI: Is it the right call? https://www.linkedin.com/pulse/opportunity-cost-allocating-capital-ai-right-call-sherwood-glazier-btzzcLinkedIn
GTBank. (2024a). December 2024 full-year investor presentation. https://gtbank-plc.files.svdcdn.com/production/financial-information/December-2024-Full-Year-Investor-Presentation_2025-04-02-174247_ncdx.pdf
GTBank. (2024b). 2024 macroeconomic review and 2025 outlook. https://gtbank-plc.files.svdcdn.com/production/outlook-insights-reports/2024-macroeconomic-review-and-2025-outlook/2024-Macroeconomic-Review-and-2025-Outlook.pdf
Guaranty Trust Bank. (2024). About us. https://www.gtbank.com/about
Guaranty Trust Bank. (2024). GTCO Plc releases 2024 half-year audited results. https://www.gtbank.com/media-centre/press-releases/gtco-plc-releases-2024-half-year-audited-results-reports-profit-before-tax-of-1-004trillion
Guaranty Trust Bank. (2024). GTCO’s Guaranty Trust Bank named best bank for corporate social responsibility in Nigeria by Euromoney. https://www.gtbank.com/investor-relations/investor-news/gtcos-guaranty-trust-bank-named-best-bank-for-corporate-social-responsibility-in-nigeria-by-euromoney
Guaranty Trust Bank. (2024). Our company ratings. https://www.gtbank.com/about/our-company/ratings
IBM. (2024). 2025 banking and financial markets outlook. IBM Institute for Business Value. https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/2025-banking-financial-markets-outlook
Infobip. (2024). GTBank: Achieving an ROI of over 200%. https://www.infobip.com/customer/gtbank
International Monetary Fund. (2024). Artificial intelligence and the future of finance. https://www.elibrary.imf.org/display/book/9798400277573/CH003.xml
Investopedia. (2013). Retail banking vs. commercial banking. https://www.investopedia.com/articles/general/071213/retail-banking-vs-commercial-banking.asp
KPMG. (2023). Unleashing potential: Exploring generative AI’s role in banking. https://kpmg.com/xx/en/our-insights/ai-and-technology/unleashing-potential-exploring-generative-ai-role-in-banking.html
McKinsey & Company. (2023). What is AI? https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-ai
McKinsey & Company. (2023). What’s next for global banking. https://www.mckinsey.com/industries/financial-services/our-insights/whats-next-for-global-banking
McKinsey & Company. (2024). Building the AI bank of the future. https://www.mckinsey.com/~/media/mckinsey/industries/financial%20services/our%20insights/building%20the%20ai%20bank%20of%20the%20future/building-the-ai-bank-of-the-future.pdf
McKinsey & Company. (2024). What is generative AI? https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-generative-ai
Metrobank. (2024). What is the time value of money? https://www.metrobank.com.ph/articles/learn/time-value-of-moneyhttps://metrobank.com.ph
Microsoft. (2024). Azure OpenAI Service pricing. https://azure.microsoft.com/en-us/pricing/details/cognitive-services/openai-service/
MORS Software. (2022). What is NII and why is it important? https://morssoftware.com/what-is-nii-and-why-is-it-important/
Nairametrics. (2024, May 13). GTBank’s retail customers’ deposits hit N3.85 trillion in 2023. https://nairametrics.com/2024/05/13/gt-banks-retail-customers-deposits-hit-n3-85-trillion-in-2023/
Nairametrics. (2025, January 29). E-payment transactions in Nigeria hit all-time high of N1.07 quadrillion in 2024. https://nairametrics.com/2025/01/29/e-payment-transactions-in-nigeria-hit-all-time-high-of-n1-07-quadrillion-in-2024/
Natural Sciences Publishing. (2024). Journal of Statistics Applications & Probability Letters, 11(2), 123–130. https://www.naturalspublishing.com/files/published/v08q0w2u3150wm.pdf
OpenAI. (2024). ChatGPT pricing. https://openai.com/chatgpt/pricing/
OpenText. (2024). State of AI in banking. https://www.opentext.com/en/media/report/state-of-ai-in-banking-digital-banking-report-en.pdfOpenText
PwC. (2023). Leveraging generative AI in banking. https://www.pwc.com/m1/en/publications/leveraging-generative-ai-in-banking.html
Research Floor. (2025). The Cobb-Douglas production function: Applicability and limitation. https://agriculture.researchfloor.org/the-cobb-douglas-production-function-applicability-and-limitation/agriculture.researchfloor.org
ResearchGate. (2024). Impact of chatbot service on bank performance based on a case study of IBM Corporation. https://www.researchgate.net/publication/383677675_Impact_of_Chatbot_Service_on_Bank_Performance_Based_on_a_Case_Study_of_IBM_Corporation
ScienceDirect. (2024). AI regulations and their impact on banking. https://www.sciencedirect.com/science/article/pii/S105752192400632X
ScienceDirect. (2024). The impact of AI on banking operations. https://www.sciencedirect.com/science/article/pii/S2405844023032814
Statista. (2024). Banking - Nigeria | Statista market forecast. https://www.statista.com/outlook/fmo/banking/nigeria
Statista. (2024). Banking - worldwide | Statista market forecast. https://www.statista.com/outlook/fmo/banking/worldwide
Statista. (2024). Quality perception of bank brands in Nigeria. https://www.statista.com/statistics/1319458/quality-perception-of-bank-brands-in-nigeria/
Stripe. (2023). Neobanks 101: What they are, how they work, and whom they are for. https://stripe.com/nl-be/resources/more/neobanks-101-what-they-are-how-they-work-and-whom-they-are-for
Synapse Energy Economics. (2020). Application of a standard approach to benefit-cost analysis for electric grid resilience investments. https://www.synapse-energy.com/sites/default/files/Standard_Approach_to_Benefit-Cost_Analysis_for__Electric_Grid_Resilience_Investments_19-007.pdfSynapse Energy
The Forage. (2022). What is retail banking? https://www.theforage.com/blog/careers/retail-banking
Zenith Bank. (2023). Zenith Bank launches intelligent chatbot ZIVA. https://www.zenithbank.com/media/news/zenith-bank-launches-intelligent-chatbot-ziva/
Glossary
1. Net Interest Income: The Net Interest Income (NII) measures a bank's profitability by assessing how effectively it leverages its interest-bearing assets and liabilities to generate revenue. It is calculated by subtracting interest expenses from interest income.
2. Artificial Intelligence: According to QuantumBlack (2024) for McKinsey, artificial intelligence (AI) is a machine’s ability to perform cognitive tasks typically associated with human minds—such as perceiving, reasoning, learning, interacting with the environment, problem-solving, and even exercising creativity.
3. Generative AI: As defined by McKinsey & Company (2024), generative AI (gen AI) refers to algorithms that create new content—such as text, images, audio, code, simulations, or videos—by learning from existing data. This contrasts with traditional predictive AI, which primarily classifies or forecasts outcomes based on established patterns.
4. Price to Book Multiples: The "price to book" (P/B) multiple, also called the market-to-book ratio (M/B), is a financial metric that compares a company's market value to its book value. It is calculated by dividing the company's market capitalization by its total book value of equity, helping to assess whether a stock is overvalued or undervalued relative to its net asset value.
5. Neobanks: Neobanks are digital-first financial institutions that operate entirely online through mobile applications and web portals. They offer a range of retail and corporate banking services—such as savings and checking accounts, loans, and digital payments—without maintaining a physical branch network.
6. Cost-Benefit Analysis (CBA): A systematic process that evaluates the economic advantages (benefits) and disadvantages (costs) of a project or investment. CBA is used to determine the net value or feasibility of implementing a new initiative by comparing expected returns against expenditures.
7. Break-even Analysis: A financial tool used to determine the point at which total revenues equal total costs. This analysis helps establish when an investment will begin to generate a net profit.
8. Net Present Value (NPV): NPV is the sum of the present values of all incoming and outgoing cash flows associated with a project over time. It is a key indicator used to assess the profitability and financial viability of an investment by discounting future cash flows to their current value.
9. Benefit-Cost Ratio (BCR): The Benefit-Cost Ratio compares the present value of a project’s benefits to its costs. A BCR greater than one suggests that the benefits outweigh the costs, indicating economic feasibility.
10. Capital Adequacy Ratio (CAR): CAR is a measure of a bank’s available capital relative to its risk-weighted assets, expressed as a percentage. It is used by regulators to ensure that banks have sufficient capital to absorb potential losses and maintain financial stability.
11. Market Structure: This term refers to the organization of a market, including the number of firms, the nature of competition, product differentiation, and barriers to entry. Understanding market structure helps in analyzing how competition affects pricing, output, and innovation in an industry.
12. Opportunity Cost: Opportunity cost represents the value of the next best alternative that is forgone when a decision is made. It reflects the benefits that could have been achieved if the alternative option had been chosen.
13. Discount Rate: The discount rate is used to convert future cash flows into their present value. It reflects the time value of money, accounts for risk, and is commonly used in financial calculations such as NPV and BCR.
14. Digital Transformation: Digital transformation involves the integration of digital technologies into all aspects of business operations. It fundamentally changes how organizations operate and deliver value to their customers, often by improving efficiency, enhancing customer experience, and driving innovation.
[...]
Acheter maintenant
- Citation du texte
- Eyitayo Olaleye (Auteur), 2025, Cost-Benefit Analysis of Deploying Generative AI Chatbots at Guaranty Trust Bank (GTBank), Nigeria, Munich, GRIN Verlag, https://www.grin.com/document/1590510
